{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.core.api import DataFrame\n",
    "import matplotlib.pyplot as plt\n",
    "from pyreadstat import pyreadstat\n",
    "import mytools\n",
    "import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df,metadata =pyreadstat.read_sav(R\"data/大学生对于开屏广告.sav\",apply_value_formats=True)\n",
    "df1,metadata = mytools.read_spss(R\"data/大学生对于开屏广告.sav\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>序号</th>\n",
       "      <th>提交答卷时间</th>\n",
       "      <th>所用时间</th>\n",
       "      <th>来源</th>\n",
       "      <th>来源详情</th>\n",
       "      <th>来自IP</th>\n",
       "      <th>@1、您的性别</th>\n",
       "      <th>@2、您每天使用手机的频率</th>\n",
       "      <th>@3、您最常遇到开屏广告的APP类型是？</th>\n",
       "      <th>@4、您一天内在手机APP上看到开屏广告的次数是</th>\n",
       "      <th>...</th>\n",
       "      <th>@10、您对手机APP开屏广告的内容印象程度是</th>\n",
       "      <th>@11、哪一类的开屏广告会吸引您的注意？【多选</th>\n",
       "      <th>@12、您是否通过APP开屏广告主动接触其介绍的产</th>\n",
       "      <th>@13、您认为APP开屏广告存在哪些问题？</th>\n",
       "      <th>@14、开屏广告是否影响了您的正常浏览体验</th>\n",
       "      <th>@15、您认为每个APP开屏广告每天出现的次数最好</th>\n",
       "      <th>@16、您觉得开屏广告的时间最好控制在</th>\n",
       "      <th>@17、您认为APP开屏广告是否侵犯了您的隐私（如</th>\n",
       "      <th>@18、您认为开屏广告中的关闭键设置是否合理</th>\n",
       "      <th>@19、关于开屏广告的关闭键设置，您认为以下哪</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [序号, 提交答卷时间, 所用时间, 来源, 来源详情, 来自IP, @1、您的性别, @2、您每天使用手机的频率, @3、您最常遇到开屏广告的APP类型是？, @4、您一天内在手机APP上看到开屏广告的次数是, @5、您对哪类APP中的开屏广告更感兴趣？, @6、遇到APP开屏广告时，您的习惯一般是, @7、您对APP开屏广告的态度是, @8、您觉得多长时间的开屏广告能接受？, @9、您对APP开屏广告所表达的产品品牌活动信息, @10、您对手机APP开屏广告的内容印象程度是, @11、哪一类的开屏广告会吸引您的注意？【多选, @12、您是否通过APP开屏广告主动接触其介绍的产, @13、您认为APP开屏广告存在哪些问题？, @14、开屏广告是否影响了您的正常浏览体验, @15、您认为每个APP开屏广告每天出现的次数最好, @16、您觉得开屏广告的时间最好控制在, @17、您认为APP开屏广告是否侵犯了您的隐私（如, @18、您认为开屏广告中的关闭键设置是否合理, @19、关于开屏广告的关闭键设置，您认为以下哪]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isnull().T.any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1.drop(columns='来源详情')\n",
    "temp = df2[df2.isnull().T.any()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16      124.119.69.67(新疆-伊犁)\n",
       "41     116.178.42.218(新疆-哈密)\n",
       "55     61.178.103.144(甘肃-兰州)\n",
       "76     61.178.103.144(甘肃-兰州)\n",
       "90     61.178.103.146(甘肃-兰州)\n",
       "94      210.26.15.146(甘肃-兰州)\n",
       "112    39.144.210.110(甘肃-兰州)\n",
       "113    61.178.103.140(甘肃-兰州)\n",
       "118    61.178.103.141(甘肃-兰州)\n",
       "127      42.88.166.65(甘肃-陇南)\n",
       "135    61.178.223.215(甘肃-兰州)\n",
       "141     42.63.135.72(宁夏-石嘴山)\n",
       "147    61.178.103.141(甘肃-兰州)\n",
       "193    61.178.103.149(甘肃-兰州)\n",
       "196    61.178.103.144(甘肃-兰州)\n",
       "204     39.144.227.84(贵州-铜仁)\n",
       "226    61.178.223.217(甘肃-兰州)\n",
       "230    61.178.103.140(甘肃-兰州)\n",
       "231     210.26.15.139(甘肃-兰州)\n",
       "240     42.63.135.72(宁夏-石嘴山)\n",
       "244    36.142.181.101(甘肃-兰州)\n",
       "258    61.178.223.210(甘肃-兰州)\n",
       "259     36.142.178.31(甘肃-兰州)\n",
       "291    61.178.103.143(甘肃-兰州)\n",
       "297     210.26.15.149(甘肃-兰州)\n",
       "298    61.178.103.149(甘肃-兰州)\n",
       "333     223.73.66.167(广东-广州)\n",
       "371     223.73.66.174(广东-广州)\n",
       "378     223.73.66.186(广东-广州)\n",
       "Name: 来自IP, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.duplicated(subset=['来自IP'],keep='first')]['来自IP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2.drop_duplicates(subset=['来自IP'],keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>序号</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>提交答卷时间</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>所用时间</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>来源</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>来自IP</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@1、您的性别</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@2、您每天使用手机的频率</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@3、您最常遇到开屏广告的APP类型是？</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@4、您一天内在手机APP上看到开屏广告的次数是</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@5、您对哪类APP中的开屏广告更感兴趣？</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@6、遇到APP开屏广告时，您的习惯一般是</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@7、您对APP开屏广告的态度是</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@8、您觉得多长时间的开屏广告能接受？</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@9、您对APP开屏广告所表达的产品品牌活动信息</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@10、您对手机APP开屏广告的内容印象程度是</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@11、哪一类的开屏广告会吸引您的注意？【多选</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@12、您是否通过APP开屏广告主动接触其介绍的产</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@13、您认为APP开屏广告存在哪些问题？</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@14、开屏广告是否影响了您的正常浏览体验</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@15、您认为每个APP开屏广告每天出现的次数最好</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@16、您觉得开屏广告的时间最好控制在</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@17、您认为APP开屏广告是否侵犯了您的隐私（如</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@18、您认为开屏广告中的关闭键设置是否合理</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>@19、关于开屏广告的关闭键设置，您认为以下哪</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                0\n",
       "序号                         object\n",
       "提交答卷时间                     object\n",
       "所用时间                       object\n",
       "来源                         object\n",
       "来自IP                       object\n",
       "@1、您的性别                    object\n",
       "@2、您每天使用手机的频率              object\n",
       "@3、您最常遇到开屏广告的APP类型是？       object\n",
       "@4、您一天内在手机APP上看到开屏广告的次数是   object\n",
       "@5、您对哪类APP中的开屏广告更感兴趣？      object\n",
       "@6、遇到APP开屏广告时，您的习惯一般是      object\n",
       "@7、您对APP开屏广告的态度是           object\n",
       "@8、您觉得多长时间的开屏广告能接受？        object\n",
       "@9、您对APP开屏广告所表达的产品品牌活动信息   object\n",
       "@10、您对手机APP开屏广告的内容印象程度是    object\n",
       "@11、哪一类的开屏广告会吸引您的注意？【多选    object\n",
       "@12、您是否通过APP开屏广告主动接触其介绍的产  object\n",
       "@13、您认为APP开屏广告存在哪些问题？      object\n",
       "@14、开屏广告是否影响了您的正常浏览体验      object\n",
       "@15、您认为每个APP开屏广告每天出现的次数最好  object\n",
       "@16、您觉得开屏广告的时间最好控制在        object\n",
       "@17、您认为APP开屏广告是否侵犯了您的隐私（如  object\n",
       "@18、您认为开屏广告中的关闭键设置是否合理     object\n",
       "@19、关于开屏广告的关闭键设置，您认为以下哪    object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = df3.rename(columns={\n",
    "    '@1、您的性别': '性别',\n",
    "    '@2、您每天使用手机的频率': '每天手机使用频率',\n",
    "    '@3、您最常遇到开屏广告的APP类型是？':'最常接触开屏广告的APP类型',\n",
    "    '@4、您一天内在手机APP上看到开屏广告的次数是':'日均接触开屏广告次数',\n",
    "    '@5、您对哪类APP中的开屏广告更感兴趣？':'开屏广告类型偏好',\n",
    "    '@6、遇到APP开屏广告时，您的习惯一般是':'开屏广告操作习惯',\n",
    "    '@7、您对APP开屏广告的态度是':'开屏广告态度',\n",
    "    '@8、您觉得多长时间的开屏广告能接受？':'开屏广告接收时长',\n",
    "    '@9、您对APP开屏广告所表达的产品品牌活动信息':'开屏广告受众自评感知效果1',\n",
    "    '@10、您对手机APP开屏广告的内容印象程度是':'开屏广告受众自评感知效果2',\n",
    "    '@11、哪一类的开屏广告会吸引您的注意？【多选':'有效开屏广告特征',\n",
    "    '@12、您是否通过APP开屏广告主动接触其介绍的产':'开屏广告受众自评行动效果',\n",
    "    '@13、您认为APP开屏广告存在哪些问题？':'开屏广告存在问题',\n",
    "    '@14、开屏广告是否影响了您的正常浏览体验':'开屏广告浏览体验',\n",
    "    '@15、您认为每个APP开屏广告每天出现的次数最好':'开屏广告建议次数',\n",
    "    '@16、您觉得开屏广告的时间最好控制在':'开屏广告建议时长',\n",
    "    '@17、您认为APP开屏广告是否侵犯了您的隐私（如':'开屏广告隐私侵犯感',\n",
    "    '@18、您认为开屏广告中的关闭键设置是否合理':'开屏广告关闭键评价',\n",
    "    '@19、关于开屏广告的关闭键设置，您认为以下哪':'开屏广告关闭键改进建议',\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2.astype({\n",
    "    '您的性别': 'category',\n",
    "    '您所在年级': 'category',\n",
    "    '填写问卷时长': 'int',\n",
    "    '您觉得网课数量多吗？': 'category',\n",
    "    '上网课过程会分心吗？': 'string',\n",
    "    '您觉得课堂教学和网络教学哪个比较好？': 'category',\n",
    "    '在上网课前，您会预先安排自己的学习过程吗？': 'category',\n",
    "    '在上网课时有不懂的问题会向老师请教吗？': 'category',\n",
    "    '在课堂，你会参加老师和同学们的讨论中吗？': 'category',\n",
    "    '你们认为线上学习的气氛如何？': 'category',\n",
    "    '您的作业完成情况如何？': 'category',\n",
    "    '您认为影响线上学习的因素有哪些？': 'category',\n",
    "    '您认为线上学习中教师教学存在的问题是？': 'category',\n",
    "    '您觉得自己线上学习效率高吗？': 'category',\n",
    "})\n",
    "df3.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.dropna(thresh=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df2.drop_duplicates(subset=['问卷编号'],keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>问卷编号</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>调查员</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>性别</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年龄</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>职业</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>伙食费</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>常去的快餐店</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>欢乐聚餐</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>快乐童年</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>都市生活</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>洋溢青春</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他感受</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士类型感知</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服务态度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康安全</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>快速便捷</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>价格昂贵</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>口感不好</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品类太少</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他评价</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>快速好吃</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服务好</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>种类多</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>价格便宜</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他吸引因素</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服务态度需改进</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>送餐速度</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>就餐环境</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品价格</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>促销活动</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品种类</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他改进因素</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>认知维度</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>情感维度</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "问卷编号       float64\n",
       "调查员         object\n",
       "性别        category\n",
       "年龄         float64\n",
       "职业          object\n",
       "伙食费       category\n",
       "常去的快餐店    category\n",
       "欢乐聚餐       float64\n",
       "快乐童年       float64\n",
       "都市生活       float64\n",
       "洋溢青春       float64\n",
       "其他感受       float64\n",
       "德克士类型感知   category\n",
       "服务态度      category\n",
       "健康安全       float64\n",
       "快速便捷       float64\n",
       "价格昂贵       float64\n",
       "口感不好       float64\n",
       "品类太少       float64\n",
       "其他评价       float64\n",
       "快速好吃       float64\n",
       "服务好        float64\n",
       "种类多        float64\n",
       "价格便宜       float64\n",
       "其他吸引因素     float64\n",
       "服务态度需改进    float64\n",
       "送餐速度       float64\n",
       "就餐环境       float64\n",
       "产品价格       float64\n",
       "促销活动       float64\n",
       "产品种类       float64\n",
       "其他改进因素     float64\n",
       "德克士企业认知度  category\n",
       "德克士广告接触度  category\n",
       "德克士就职意愿   category\n",
       "德克士股票     category\n",
       "德克士综合评价   category\n",
       "肯德基企业认知度  category\n",
       "肯德基广告接触度  category\n",
       "肯德基就职意愿   category\n",
       "肯德基股票     category\n",
       "肯德基综合评价   category\n",
       "麦当劳企业认知度  category\n",
       "麦当劳广告接触度  category\n",
       "麦当劳就职意愿   category\n",
       "麦当劳股票     category\n",
       "麦当劳综合评价   category\n",
       "必胜客企业认知度  category\n",
       "必胜客广告接触度  category\n",
       "必胜客就职意愿   category\n",
       "必胜客股票     category\n",
       "必胜客综合评价   category\n",
       "认知维度       float64\n",
       "情感维度       float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['性别', '伙食费', '常去的快餐店', '德克士类型感知', '服务态度', '德克士企业认知度', '德克士广告接触度',\n",
       "       '德克士就职意愿', '德克士股票', '德克士综合评价', '肯德基企业认知度', '肯德基广告接触度', '肯德基就职意愿',\n",
       "       '肯德基股票', '肯德基综合评价', '麦当劳企业认知度', '麦当劳广告接触度', '麦当劳就职意愿', '麦当劳股票',\n",
       "       '麦当劳综合评价', '必胜客企业认知度', '必胜客广告接触度', '必胜客就职意愿', '必胜客股票', '必胜客综合评价'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.columns[df3.dtypes=='category']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>问卷编号</th>\n",
       "      <td>Int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>调查员</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>性别</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年龄</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>职业</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>伙食费</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>常去的快餐店</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>欢乐聚餐</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>快乐童年</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>都市生活</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>洋溢青春</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他感受</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士类型感知</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服务态度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康安全</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>快速便捷</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>价格昂贵</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>口感不好</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品类太少</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他评价</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>快速好吃</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服务好</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>种类多</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>价格便宜</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他吸引因素</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服务态度需改进</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>送餐速度</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>就餐环境</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品价格</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>促销活动</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品种类</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他改进因素</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>认知维度</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>情感维度</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "问卷编号         Int64\n",
       "调查员         object\n",
       "性别        category\n",
       "年龄         float64\n",
       "职业          object\n",
       "伙食费       category\n",
       "常去的快餐店    category\n",
       "欢乐聚餐       float64\n",
       "快乐童年       float64\n",
       "都市生活       float64\n",
       "洋溢青春       float64\n",
       "其他感受       float64\n",
       "德克士类型感知   category\n",
       "服务态度      category\n",
       "健康安全       float64\n",
       "快速便捷       float64\n",
       "价格昂贵       float64\n",
       "口感不好       float64\n",
       "品类太少       float64\n",
       "其他评价       float64\n",
       "快速好吃       float64\n",
       "服务好        float64\n",
       "种类多        float64\n",
       "价格便宜       float64\n",
       "其他吸引因素     float64\n",
       "服务态度需改进    float64\n",
       "送餐速度       float64\n",
       "就餐环境       float64\n",
       "产品价格       float64\n",
       "促销活动       float64\n",
       "产品种类       float64\n",
       "其他改进因素     float64\n",
       "德克士企业认知度  category\n",
       "德克士广告接触度  category\n",
       "德克士就职意愿   category\n",
       "德克士股票     category\n",
       "德克士综合评价   category\n",
       "肯德基企业认知度  category\n",
       "肯德基广告接触度  category\n",
       "肯德基就职意愿   category\n",
       "肯德基股票     category\n",
       "肯德基综合评价   category\n",
       "麦当劳企业认知度  category\n",
       "麦当劳广告接触度  category\n",
       "麦当劳就职意愿   category\n",
       "麦当劳股票     category\n",
       "麦当劳综合评价   category\n",
       "必胜客企业认知度  category\n",
       "必胜客广告接触度  category\n",
       "必胜客就职意愿   category\n",
       "必胜客股票     category\n",
       "必胜客综合评价   category\n",
       "认知维度       float64\n",
       "情感维度       float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df2.astype({'问卷编号':'Int64'})\n",
    "df4.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>问卷编号</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>调查员</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>性别</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年龄</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>职业</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>伙食费</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>常去的快餐店</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>欢乐聚餐</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>快乐童年</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>都市生活</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>洋溢青春</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他感受</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士类型感知</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服务态度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康安全</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>快速便捷</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>价格昂贵</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>口感不好</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品类太少</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他评价</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>快速好吃</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服务好</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>种类多</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>价格便宜</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他吸引因素</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服务态度需改进</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>送餐速度</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>就餐环境</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品价格</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>促销活动</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品种类</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他改进因素</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>认知维度</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>情感维度</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "问卷编号       float64\n",
       "调查员         object\n",
       "性别        category\n",
       "年龄         float64\n",
       "职业          object\n",
       "伙食费       category\n",
       "常去的快餐店    category\n",
       "欢乐聚餐       float64\n",
       "快乐童年       float64\n",
       "都市生活       float64\n",
       "洋溢青春       float64\n",
       "其他感受       float64\n",
       "德克士类型感知   category\n",
       "服务态度      category\n",
       "健康安全       float64\n",
       "快速便捷       float64\n",
       "价格昂贵       float64\n",
       "口感不好       float64\n",
       "品类太少       float64\n",
       "其他评价       float64\n",
       "快速好吃       float64\n",
       "服务好        float64\n",
       "种类多        float64\n",
       "价格便宜       float64\n",
       "其他吸引因素     float64\n",
       "服务态度需改进    float64\n",
       "送餐速度       float64\n",
       "就餐环境       float64\n",
       "产品价格       float64\n",
       "促销活动       float64\n",
       "产品种类       float64\n",
       "其他改进因素     float64\n",
       "德克士企业认知度  category\n",
       "德克士广告接触度  category\n",
       "德克士就职意愿   category\n",
       "德克士股票     category\n",
       "德克士综合评价   category\n",
       "肯德基企业认知度  category\n",
       "肯德基广告接触度  category\n",
       "肯德基就职意愿   category\n",
       "肯德基股票     category\n",
       "肯德基综合评价   category\n",
       "麦当劳企业认知度  category\n",
       "麦当劳广告接触度  category\n",
       "麦当劳就职意愿   category\n",
       "麦当劳股票     category\n",
       "麦当劳综合评价   category\n",
       "必胜客企业认知度  category\n",
       "必胜客广告接触度  category\n",
       "必胜客就职意愿   category\n",
       "必胜客股票     category\n",
       "必胜客综合评价   category\n",
       "认知维度       float64\n",
       "情感维度       float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df3.astype({'年龄':'Int64'})\n",
    "df3.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>问卷编号</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>调查员</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>性别</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年龄</th>\n",
       "      <td>Int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>职业</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>伙食费</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>常去的快餐店</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>欢乐聚餐</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>快乐童年</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>都市生活</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>洋溢青春</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他感受</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士类型感知</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服务态度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康安全</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>快速便捷</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>价格昂贵</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>口感不好</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品类太少</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他评价</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>快速好吃</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服务好</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>种类多</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>价格便宜</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他吸引因素</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服务态度需改进</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>送餐速度</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>就餐环境</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品价格</th>\n",
       "      <td>Int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>促销活动</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品种类</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他改进因素</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>认知维度</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>情感维度</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "问卷编号       float64\n",
       "调查员         object\n",
       "性别        category\n",
       "年龄           Int64\n",
       "职业          object\n",
       "伙食费       category\n",
       "常去的快餐店    category\n",
       "欢乐聚餐       float64\n",
       "快乐童年       float64\n",
       "都市生活       float64\n",
       "洋溢青春       float64\n",
       "其他感受       float64\n",
       "德克士类型感知   category\n",
       "服务态度      category\n",
       "健康安全       float64\n",
       "快速便捷       float64\n",
       "价格昂贵       float64\n",
       "口感不好       float64\n",
       "品类太少       float64\n",
       "其他评价       float64\n",
       "快速好吃       float64\n",
       "服务好        float64\n",
       "种类多        float64\n",
       "价格便宜       float64\n",
       "其他吸引因素     float64\n",
       "服务态度需改进    float64\n",
       "送餐速度       float64\n",
       "就餐环境       float64\n",
       "产品价格         Int64\n",
       "促销活动       float64\n",
       "产品种类       float64\n",
       "其他改进因素     float64\n",
       "德克士企业认知度  category\n",
       "德克士广告接触度  category\n",
       "德克士就职意愿   category\n",
       "德克士股票     category\n",
       "德克士综合评价   category\n",
       "肯德基企业认知度  category\n",
       "肯德基广告接触度  category\n",
       "肯德基就职意愿   category\n",
       "肯德基股票     category\n",
       "肯德基综合评价   category\n",
       "麦当劳企业认知度  category\n",
       "麦当劳广告接触度  category\n",
       "麦当劳就职意愿   category\n",
       "麦当劳股票     category\n",
       "麦当劳综合评价   category\n",
       "必胜客企业认知度  category\n",
       "必胜客广告接触度  category\n",
       "必胜客就职意愿   category\n",
       "必胜客股票     category\n",
       "必胜客综合评价   category\n",
       "认知维度       float64\n",
       "情感维度       float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df4.astype({'产品价格':'Int64'})\n",
    "df4.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>问卷编号</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>调查员</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>性别</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>年龄</th>\n",
       "      <td>Int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>职业</th>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>伙食费</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>常去的快餐店</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>欢乐聚餐</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>快乐童年</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>都市生活</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>洋溢青春</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他感受</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士类型感知</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服务态度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>健康安全</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>快速便捷</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>价格昂贵</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>口感不好</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>品类太少</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他评价</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>快速好吃</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服务好</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>种类多</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>价格便宜</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他吸引因素</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>服务态度需改进</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>送餐速度</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>就餐环境</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品价格</th>\n",
       "      <td>Int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>促销活动</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>产品种类</th>\n",
       "      <td>Int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>其他改进因素</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>德克士综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>肯德基综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>麦当劳综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客企业认知度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客广告接触度</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客就职意愿</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客股票</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>必胜客综合评价</th>\n",
       "      <td>category</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>认知维度</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>情感维度</th>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0\n",
       "问卷编号       float64\n",
       "调查员         object\n",
       "性别        category\n",
       "年龄           Int64\n",
       "职业          object\n",
       "伙食费       category\n",
       "常去的快餐店    category\n",
       "欢乐聚餐       float64\n",
       "快乐童年       float64\n",
       "都市生活       float64\n",
       "洋溢青春       float64\n",
       "其他感受       float64\n",
       "德克士类型感知   category\n",
       "服务态度      category\n",
       "健康安全       float64\n",
       "快速便捷       float64\n",
       "价格昂贵       float64\n",
       "口感不好       float64\n",
       "品类太少       float64\n",
       "其他评价       float64\n",
       "快速好吃       float64\n",
       "服务好        float64\n",
       "种类多        float64\n",
       "价格便宜       float64\n",
       "其他吸引因素     float64\n",
       "服务态度需改进    float64\n",
       "送餐速度       float64\n",
       "就餐环境       float64\n",
       "产品价格         Int64\n",
       "促销活动       float64\n",
       "产品种类         Int64\n",
       "其他改进因素     float64\n",
       "德克士企业认知度  category\n",
       "德克士广告接触度  category\n",
       "德克士就职意愿   category\n",
       "德克士股票     category\n",
       "德克士综合评价   category\n",
       "肯德基企业认知度  category\n",
       "肯德基广告接触度  category\n",
       "肯德基就职意愿   category\n",
       "肯德基股票     category\n",
       "肯德基综合评价   category\n",
       "麦当劳企业认知度  category\n",
       "麦当劳广告接触度  category\n",
       "麦当劳就职意愿   category\n",
       "麦当劳股票     category\n",
       "麦当劳综合评价   category\n",
       "必胜客企业认知度  category\n",
       "必胜客广告接触度  category\n",
       "必胜客就职意愿   category\n",
       "必胜客股票     category\n",
       "必胜客综合评价   category\n",
       "认知维度       float64\n",
       "情感维度       float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = df4.astype({'产品种类':'Int64'})\n",
    "df5.dtypes.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "一般    205\n",
       "不错    132\n",
       "很好     30\n",
       "不好     15\n",
       "Name: 服务态度, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5['服务态度'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "男性    192\n",
      "女性    190\n",
      "Name: 性别, dtype: int64\n",
      "六百到九百     126\n",
      "九百到一千二    105\n",
      "一千二以上      80\n",
      "三百到六百      71\n",
      "Name: 伙食费, dtype: int64\n",
      "肯德基       116\n",
      "德克士        77\n",
      "从不去快餐店     74\n",
      "麦当劳        55\n",
      "必胜客        41\n",
      "其他         19\n",
      "Name: 常去的快餐店, dtype: int64\n",
      "普遍大众    175\n",
      "时尚新颖     82\n",
      "中西合璧     79\n",
      "古板单调     29\n",
      "其他类型     17\n",
      "Name: 德克士类型感知, dtype: int64\n",
      "一般    205\n",
      "不错    132\n",
      "很好     30\n",
      "不好     15\n",
      "Name: 服务态度, dtype: int64\n",
      "一点印象都没有    116\n",
      "只知道名字      115\n",
      "只知道几项       86\n",
      "大致了解        58\n",
      "非常了解         7\n",
      "Name: 德克士企业认知度, dtype: int64\n",
      "偶尔看到     146\n",
      "从未看过     102\n",
      "有时会看到     86\n",
      "常常会看到     48\n",
      "Name: 德克士广告接触度, dtype: int64\n",
      "不想去            150\n",
      "到该公司也不错        110\n",
      "不知道             84\n",
      "一定想办法到该公司工作     38\n",
      "Name: 德克士就职意愿, dtype: int64\n",
      "不想买      143\n",
      "不知道      104\n",
      "买了也不错     87\n",
      "一定会买      48\n",
      "Name: 德克士股票, dtype: int64\n",
      "二流     151\n",
      "一流      81\n",
      "不知道     76\n",
      "三流      74\n",
      "Name: 德克士综合评价, dtype: int64\n",
      "只知道名字      127\n",
      "只知道几项      124\n",
      "大致了解        82\n",
      "一点印象都没有     33\n",
      "非常了解        16\n",
      "Name: 肯德基企业认知度, dtype: int64\n",
      "偶尔看到     115\n",
      "常常会看到    115\n",
      "有时会看到    108\n",
      "从未看过      44\n",
      "Name: 肯德基广告接触度, dtype: int64\n",
      "到该公司也不错        133\n",
      "不想去            129\n",
      "不知道             83\n",
      "一定想办法到该公司工作     37\n",
      "Name: 肯德基就职意愿, dtype: int64\n",
      "买了也不错    125\n",
      "不想买      111\n",
      "不知道       96\n",
      "一定会买      50\n",
      "Name: 肯德基股票, dtype: int64\n",
      "二流     180\n",
      "一流     122\n",
      "不知道     50\n",
      "三流      30\n",
      "Name: 肯德基综合评价, dtype: int64\n",
      "只知道几项      140\n",
      "只知道名字      126\n",
      "大致了解        76\n",
      "一点印象都没有     35\n",
      "非常了解         5\n",
      "Name: 麦当劳企业认知度, dtype: int64\n",
      "偶尔看到     123\n",
      "有时会看到    121\n",
      "常常会看到     97\n",
      "从未看过      41\n",
      "Name: 麦当劳广告接触度, dtype: int64\n",
      "不想去            136\n",
      "到该公司也不错        133\n",
      "不知道             81\n",
      "一定想办法到该公司工作     32\n",
      "Name: 麦当劳就职意愿, dtype: int64\n",
      "不想买      127\n",
      "不知道      114\n",
      "买了也不错    107\n",
      "一定会买      34\n",
      "Name: 麦当劳股票, dtype: int64\n",
      "二流     156\n",
      "一流     112\n",
      "不知道     77\n",
      "三流      37\n",
      "Name: 麦当劳综合评价, dtype: int64\n",
      "只知道名字      140\n",
      "只知道几项       99\n",
      "一点印象都没有     73\n",
      "大致了解        58\n",
      "非常了解        12\n",
      "Name: 必胜客企业认知度, dtype: int64\n",
      "偶尔看到     123\n",
      "有时会看到    111\n",
      "从未看过      82\n",
      "常常会看到     66\n",
      "Name: 必胜客广告接触度, dtype: int64\n",
      "不想去            150\n",
      "不知道            116\n",
      "到该公司也不错         87\n",
      "一定想办法到该公司工作     29\n",
      "Name: 必胜客就职意愿, dtype: int64\n",
      "不知道      140\n",
      "不想买      122\n",
      "买了也不错     85\n",
      "一定会买      35\n",
      "Name: 必胜客股票, dtype: int64\n",
      "二流     137\n",
      "一流     103\n",
      "不知道     86\n",
      "三流      56\n",
      "Name: 必胜客综合评价, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "for col in df5.columns[df5.dtypes=='category']:\n",
    "    print(df5[col].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "男性    192\n",
      "女性    190\n",
      "Name: 性别, dtype: int64\n",
      "六百到九百     126\n",
      "九百到一千二    105\n",
      "一千二以上      80\n",
      "三百到六百      71\n",
      "Name: 伙食费, dtype: int64\n",
      "肯德基       116\n",
      "德克士        77\n",
      "从不去快餐店     74\n",
      "麦当劳        55\n",
      "必胜客        41\n",
      "其他         19\n",
      "Name: 常去的快餐店, dtype: int64\n",
      "普遍大众    175\n",
      "时尚新颖     82\n",
      "中西合璧     79\n",
      "古板单调     29\n",
      "其他类型     17\n",
      "Name: 德克士类型感知, dtype: int64\n",
      "一般    205\n",
      "不错    132\n",
      "很好     30\n",
      "不好     15\n",
      "Name: 服务态度, dtype: int64\n",
      "一点印象都没有    116\n",
      "只知道名字      115\n",
      "只知道几项       86\n",
      "大致了解        58\n",
      "非常了解         7\n",
      "Name: 德克士企业认知度, dtype: int64\n",
      "偶尔看到     146\n",
      "从未看过     102\n",
      "有时会看到     86\n",
      "常常会看到     48\n",
      "Name: 德克士广告接触度, dtype: int64\n",
      "不想去            150\n",
      "到该公司也不错        110\n",
      "不知道             84\n",
      "一定想办法到该公司工作     38\n",
      "Name: 德克士就职意愿, dtype: int64\n",
      "不想买      143\n",
      "不知道      104\n",
      "买了也不错     87\n",
      "一定会买      48\n",
      "Name: 德克士股票, dtype: int64\n",
      "二流     151\n",
      "一流      81\n",
      "不知道     76\n",
      "三流      74\n",
      "Name: 德克士综合评价, dtype: int64\n",
      "只知道名字      127\n",
      "只知道几项      124\n",
      "大致了解        82\n",
      "一点印象都没有     33\n",
      "非常了解        16\n",
      "Name: 肯德基企业认知度, dtype: int64\n",
      "偶尔看到     115\n",
      "常常会看到    115\n",
      "有时会看到    108\n",
      "从未看过      44\n",
      "Name: 肯德基广告接触度, dtype: int64\n",
      "到该公司也不错        133\n",
      "不想去            129\n",
      "不知道             83\n",
      "一定想办法到该公司工作     37\n",
      "Name: 肯德基就职意愿, dtype: int64\n",
      "买了也不错    125\n",
      "不想买      111\n",
      "不知道       96\n",
      "一定会买      50\n",
      "Name: 肯德基股票, dtype: int64\n",
      "二流     180\n",
      "一流     122\n",
      "不知道     50\n",
      "三流      30\n",
      "Name: 肯德基综合评价, dtype: int64\n",
      "只知道几项      140\n",
      "只知道名字      126\n",
      "大致了解        76\n",
      "一点印象都没有     35\n",
      "非常了解         5\n",
      "Name: 麦当劳企业认知度, dtype: int64\n",
      "偶尔看到     123\n",
      "有时会看到    121\n",
      "常常会看到     97\n",
      "从未看过      41\n",
      "Name: 麦当劳广告接触度, dtype: int64\n",
      "不想去            136\n",
      "到该公司也不错        133\n",
      "不知道             81\n",
      "一定想办法到该公司工作     32\n",
      "Name: 麦当劳就职意愿, dtype: int64\n",
      "不想买      127\n",
      "不知道      114\n",
      "买了也不错    107\n",
      "一定会买      34\n",
      "Name: 麦当劳股票, dtype: int64\n",
      "二流     156\n",
      "一流     112\n",
      "不知道     77\n",
      "三流      37\n",
      "Name: 麦当劳综合评价, dtype: int64\n",
      "只知道名字      140\n",
      "只知道几项       99\n",
      "一点印象都没有     73\n",
      "大致了解        58\n",
      "非常了解        12\n",
      "Name: 必胜客企业认知度, dtype: int64\n",
      "偶尔看到     123\n",
      "有时会看到    111\n",
      "从未看过      82\n",
      "常常会看到     66\n",
      "Name: 必胜客广告接触度, dtype: int64\n",
      "不想去            150\n",
      "不知道            116\n",
      "到该公司也不错         87\n",
      "一定想办法到该公司工作     29\n",
      "Name: 必胜客就职意愿, dtype: int64\n",
      "不知道      140\n",
      "不想买      122\n",
      "买了也不错     85\n",
      "一定会买      35\n",
      "Name: 必胜客股票, dtype: int64\n",
      "二流     137\n",
      "一流     103\n",
      "不知道     86\n",
      "三流      56\n",
      "Name: 必胜客综合评价, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "mytools.print_all_cats(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('性别==22')\n",
    "df5.loc[322,'性别'] = '女性'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('服务态度==0.0')\n",
    "df5.loc[304,'服务态度'] = '不错'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('德克士综合评价==0.0')\n",
    "df5.loc[253,'德克士综合评价'] = '一流'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df5.query('麦当劳广告接触度==5.0')\n",
    "df5.loc[105,'麦当劳广告接触度'] = '从未看过'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    382.000000\n",
       "mean      24.222513\n",
       "std        6.429935\n",
       "min       10.000000\n",
       "25%       20.000000\n",
       "50%       22.000000\n",
       "75%       26.000000\n",
       "max       60.000000\n",
       "Name: 年龄, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"年龄\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count       382.000000\n",
      "mean        533.633508\n",
      "std        5105.791101\n",
      "min           1.000000\n",
      "25%         143.250000\n",
      "50%         260.500000\n",
      "75%         388.750000\n",
      "max      100000.000000\n",
      "Name: 问卷编号, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.269634\n",
      "std        0.444351\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 欢乐聚餐, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.136126\n",
      "std        0.343372\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 快乐童年, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.298429\n",
      "std        0.458169\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 都市生活, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.149215\n",
      "std        0.356767\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 洋溢青春, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.191099\n",
      "std        0.393683\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他感受, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.146597\n",
      "std        0.354167\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 健康安全, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.562827\n",
      "std        0.496688\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        1.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 快速便捷, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.324607\n",
      "std        0.468842\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 价格昂贵, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.167539\n",
      "std        0.373946\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 口感不好, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.167539\n",
      "std        0.373946\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 品类太少, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.081152\n",
      "std        0.273426\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他评价, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.458115\n",
      "std        0.498896\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 快速好吃, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.188482\n",
      "std        0.391609\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 服务好, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.295812\n",
      "std        0.457005\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 种类多, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.159686\n",
      "std        0.366795\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 价格便宜, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.089005\n",
      "std        0.285125\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他吸引因素, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.319372\n",
      "std        0.466845\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 服务态度需改进, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.303665\n",
      "std        0.460443\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 送餐速度, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.361257\n",
      "std        0.480995\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n",
      "Name: 就餐环境, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.232984\n",
      "std        0.423287\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 促销活动, dtype: float64\n",
      "count    382.000000\n",
      "mean       0.049738\n",
      "std        0.217689\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        0.000000\n",
      "max        1.000000\n",
      "Name: 其他改进因素, dtype: float64\n",
      "count    382.000000\n",
      "mean       6.196335\n",
      "std        2.988286\n",
      "min        0.000000\n",
      "25%        4.000000\n",
      "50%        7.000000\n",
      "75%        9.000000\n",
      "max       11.000000\n",
      "Name: 认知维度, dtype: float64\n",
      "count    382.000000\n",
      "mean       7.020942\n",
      "std        2.602156\n",
      "min        0.000000\n",
      "25%        5.000000\n",
      "50%        7.000000\n",
      "75%        9.000000\n",
      "max       12.000000\n",
      "Name: 情感维度, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "mytools.print_all_int(df5)\n",
    "mytools.print_all_float(df5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = '麦当劳企业认知度 == \"大致了解\" and 麦当劳广告接触度 == \"偶尔看到\"'\n",
    "temp = df5.query(c1)[['麦当劳企业认知度','麦当劳广告接触度']]\n",
    "df6 = df5.drop(temp.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df6.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = df5['常去的快餐店'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>常去的快餐店</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>肯德基</td>\n",
       "      <td>116</td>\n",
       "      <td>30.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>德克士</td>\n",
       "      <td>77</td>\n",
       "      <td>20.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>从不去快餐店</td>\n",
       "      <td>74</td>\n",
       "      <td>19.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>麦当劳</td>\n",
       "      <td>55</td>\n",
       "      <td>14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>必胜客</td>\n",
       "      <td>41</td>\n",
       "      <td>10.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>其他</td>\n",
       "      <td>19</td>\n",
       "      <td>4.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>总和</td>\n",
       "      <td>382</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   常去的快餐店   个数    百分比\n",
       "0     肯德基  116  30.37\n",
       "1     德克士   77  20.16\n",
       "2  从不去快餐店   74  19.37\n",
       "3     麦当劳   55   14.4\n",
       "4     必胜客   41  10.73\n",
       "5      其他   19   4.97\n",
       "6      总和  382  100.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = pd.DataFrame()\n",
    "b['常去的快餐店'] = df5['常去的快餐店'].value_counts().index\n",
    "b['个数'] = df5['常去的快餐店'].value_counts().values\n",
    "b['百分比'] = df5['常去的快餐店'].value_counts(normalize=True).values * 100\n",
    "b['百分比'] = b['百分比'].apply(lambda x: round(x, 2))\n",
    "total_row = pd.Series({'常去的快餐店':'总和','个数':b['个数'].sum(),'百分比':b['百分比'].sum()}).to_frame().T\n",
    "pd.concat([b,total_row],ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>常去的快餐店</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>肯德基</td>\n",
       "      <td>116</td>\n",
       "      <td>30.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>德克士</td>\n",
       "      <td>77</td>\n",
       "      <td>20.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>从不去快餐店</td>\n",
       "      <td>74</td>\n",
       "      <td>19.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>麦当劳</td>\n",
       "      <td>55</td>\n",
       "      <td>14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>必胜客</td>\n",
       "      <td>41</td>\n",
       "      <td>10.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>其他</td>\n",
       "      <td>19</td>\n",
       "      <td>4.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>总和</td>\n",
       "      <td>382</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   常去的快餐店   个数    百分比\n",
       "0     肯德基  116  30.37\n",
       "1     德克士   77  20.16\n",
       "2  从不去快餐店   74  19.37\n",
       "3     麦当劳   55   14.4\n",
       "4     必胜客   41  10.73\n",
       "5      其他   19   4.97\n",
       "6      总和  382  100.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mytools.gen_percent_table(df5,'常去的快餐店')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.show_bar(df5,'常去的快餐店')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>麦当劳综合评价</th>\n",
       "      <th>个数</th>\n",
       "      <th>百分比</th>\n",
       "      <th>累计百分比（%）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>一流</td>\n",
       "      <td>110</td>\n",
       "      <td>29.33</td>\n",
       "      <td>29.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>二流</td>\n",
       "      <td>153</td>\n",
       "      <td>40.8</td>\n",
       "      <td>70.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>三流</td>\n",
       "      <td>37</td>\n",
       "      <td>9.87</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>不知道</td>\n",
       "      <td>75</td>\n",
       "      <td>20.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>总和</td>\n",
       "      <td>375</td>\n",
       "      <td>100.0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  麦当劳综合评价   个数    百分比 累计百分比（%）\n",
       "0      一流  110  29.33    29.33\n",
       "1      二流  153   40.8    70.13\n",
       "2      三流   37   9.87     80.0\n",
       "3     不知道   75   20.0    100.0\n",
       "4      总和  375  100.0         "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.api.types import CategoricalDtype\n",
    "cat_dtype = CategoricalDtype(\n",
    "    categories=['一流','二流', '三流','不知道'], ordered=True)\n",
    "df = df.astype({'麦当劳综合评价':cat_dtype})\n",
    "mytools.ordinal_desc(df,'麦当劳综合评价')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.show_bar(df,'麦当劳综合评价',sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    375.000000\n",
       "mean       6.429333\n",
       "std        3.083455\n",
       "min        0.000000\n",
       "25%        4.000000\n",
       "50%        7.000000\n",
       "75%        9.000000\n",
       "max       12.000000\n",
       "Name: 认知维度, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['认知维度']= df['德克士综合评价'].cat.codes + df['肯德基综合评价'].cat.codes + df['麦当劳综合评价'].cat.codes+ df['必胜客综合评价'].cat.codes\n",
    "df['认知维度'].describe()\n",
    "df['认知维度'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='认知维度', ylabel='Count'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.histplot(data=df, x=\"认知维度\",binwidth=1,kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = pd.crosstab(\n",
    "        df['伙食费'],\n",
    "        df['调查员'],\n",
    "        normalize='columns',\n",
    "        margins=True,\n",
    "        margins_name='合计',\n",
    "    )*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'tau_y值为：0.141，该值属于极弱相关或不相关。'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tau_y = mytools.goodmanKruska_tau_y(df, '调查员', '伙食费')\n",
    "f'tau_y值为：{tau_y:.3f}，该值属于{mytools.draw_on_corr(tau_y)}。'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>职业</th>\n",
       "      <th></th>\n",
       "      <th>个体</th>\n",
       "      <th>个体户</th>\n",
       "      <th>促销员</th>\n",
       "      <th>保安</th>\n",
       "      <th>公关经理</th>\n",
       "      <th>公务员</th>\n",
       "      <th>公司职员</th>\n",
       "      <th>农民工</th>\n",
       "      <th>医生</th>\n",
       "      <th>...</th>\n",
       "      <th>警察</th>\n",
       "      <th>退休</th>\n",
       "      <th>送饮员</th>\n",
       "      <th>部门主管</th>\n",
       "      <th>银行</th>\n",
       "      <th>银行白领</th>\n",
       "      <th>银行职员</th>\n",
       "      <th>销售</th>\n",
       "      <th>餐饮</th>\n",
       "      <th>合计</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>伙食费</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>六百到九百</th>\n",
       "      <td>42.86</td>\n",
       "      <td>35.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>九百到一千二</th>\n",
       "      <td>42.86</td>\n",
       "      <td>28.57</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>三百到六百</th>\n",
       "      <td>0.00</td>\n",
       "      <td>7.14</td>\n",
       "      <td>16.67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>18.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>一千二以上</th>\n",
       "      <td>14.29</td>\n",
       "      <td>28.57</td>\n",
       "      <td>50.00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>66.67</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "职业                个体    个体户    促销员     保安   公关经理    公务员   公司职员    农民工     医生  \\\n",
       "伙食费                                                                            \n",
       "六百到九百   42.86  35.71   0.00    0.0  33.33    0.0    0.0   0.00  100.0    0.0   \n",
       "九百到一千二  42.86  28.57  33.33    0.0   0.00    0.0    0.0  66.67    0.0  100.0   \n",
       "三百到六百    0.00   7.14  16.67    0.0   0.00    0.0  100.0   0.00    0.0    0.0   \n",
       "一千二以上   14.29  28.57  50.00  100.0  66.67  100.0    0.0  33.33    0.0    0.0   \n",
       "\n",
       "职业      ...     警察     退休    送饮员   部门主管     银行   银行白领   银行职员    销售     餐饮  \\\n",
       "伙食费     ...                                                                 \n",
       "六百到九百   ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   0.0    0.0   \n",
       "九百到一千二  ...  100.0  100.0    0.0    0.0    0.0    0.0  100.0  25.0    0.0   \n",
       "三百到六百   ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0   0.0  100.0   \n",
       "一千二以上   ...    0.0    0.0  100.0  100.0  100.0  100.0    0.0  75.0    0.0   \n",
       "\n",
       "职业         合计  \n",
       "伙食费            \n",
       "六百到九百   33.33  \n",
       "九百到一千二  26.93  \n",
       "三百到六百   18.67  \n",
       "一千二以上   21.07  \n",
       "\n",
       "[4 rows x 61 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_dtype = CategoricalDtype(\n",
    "    categories=['六百到九百','九百到一千二', '三百到六百','一千二以上'], ordered=True)\n",
    "df = df.astype({'伙食费':cat_dtype})\n",
    "\n",
    "result = pd.crosstab(\n",
    "        df['伙食费'],\n",
    "        df['职业'],\n",
    "        normalize='columns',\n",
    "        margins=True,\n",
    "        margins_name='合计',\n",
    "    )*100\n",
    "result.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['情感维度']= df['德克士就职意愿'].cat.codes + df['肯德基就职意愿'].cat.codes + df['麦当劳就职意愿'].cat.codes+ df['必胜客就职意愿'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mytools.gen_scatter(df,'情感维度','认知维度')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='常去的快餐店', ylabel='认知维度'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(\n",
    "        x='常去的快餐店',\n",
    "        y='认知维度',\n",
    "        data=df,\n",
    "        color='white',\n",
    "        linewidth=1,\n",
    "        width=0.5,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'相关比率为：0.024，按照J.Cohen 提出的标准(0.01时为小效应，0.06时为中等效应，而0.14为大效应)，强度为低度相关。'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from statsmodels.formula.api import ols\n",
    "model = ols('认知维度 ~ 常去的快餐店', df).fit()\n",
    "eta_2 = model.rsquared\n",
    "f\"\"\"相关比率为：{eta_2:.3f}，按照J.Cohen 提出的标准(0.01时为小效应，0.06时为中等效应，而0.14为大效应)，强度为{mytools.draw_on_eta2(eta_2)}。\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = mytools.set_label_to_code(df, metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "mytools.to_sav(df_clean,metadata,'./data/企业形象调查原始数据.sav')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10 (tags/v3.8.10:3d8993a, May  3 2021, 11:48:03) [MSC v.1928 64 bit (AMD64)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "e5b51f9075b4cc1ea8d9810577a26807122690438b3a6e6e05129a402faed2ba"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
